The present invention relates to a system and method for predicting space weather, and in particular, for predicting solar-induced disturbances of the environment of near-earth space, such as geomagnetic storms. More specifically, the invention relates to predicting space weather based on the behavior of solar energetic particles (xe2x80x9cSEPsxe2x80x9d), which are created by the sun or by shocks and which precede solar-induced disturbances, and on solar, interplanetary and geophysical data.
When solar flares, disappearing filaments, and other solar events occur on the sun they create great turbulences and disturbances in the region of space close to the sun. These disturbances are often so extreme that they create shock waves which travel through space and, ultimately, arrive at the earth or at other locations of interest (e.g. a spacecraft position, a comet, or a planet), where they can cause serious problems such as loss of spacecraft, spacecraft anomalies (such as bit flips in electronic components), surface charging problems, disruption of on-board computer memories, and even damage to the structure of semi-conductor microelectronics and solar cells. The charged particles, including energetic electrons and protons, associated with these disturbances can do as much damage to solar cells and other hardware in one disturbance as several years"" exposure to the undisturbed environment. For example, energetic electrons can cause large static charges, some measuring as high as 19,000 volts, to build up in insulators deep in spacecraft, which may cause arcing that damage sensitive electronic components. In addition, astronauts both inside and outside a spacecraft, space station or shuttle can be subjected to dangerous doses of protons and other types of radiation during these disturbances.
These disturbances can also cause communications blackouts at all frequencies, not only with spacecraft, but with high-flying aircraft and with ground-based objects. High frequency (HF) radio wave communication is more routinely affected since it depends on reflection from the ionosphere to carry signals great distances. Ionospheric irregularities caused by solar disturbances give rise to signal dispersion, fading, and even complete signal loss during very disturbed conditions. Ionospheric irregularities also affect the higher frequency radio waves used by telecommunication companies that penetrate the ionosphere and are relayed via satellite to other locations. The ionospheric irregularities can even prohibit critical communications such as search and rescue efforts and military operations.
One example of a serious space weather related communications failure took place in the early 1980s when President Reagan was on Air Force One on his way to Chinaxe2x80x94all communications were lost with the plane for more than two hours. Mr. Reagan and his advisors were upset and concerned; they were subsequently informed that the failure was due to disturbances that originated on the sun and eventually propagated to the near earth environment.
In addition to communications systems, marine navigation systems using very low frequency signals, such as LORAN and OMEGA, depend on accurate information on the altitude of the bottom of the ionosphere. During environmental disturbances, rapid vertical changes occur in the location of this boundary, introducing significant errors of up to several kilometers in determinations of location.
Global Positioning Systems (GPS) are also sensitive to space weather disturbances. These systems have a wide variety of applications including aircraft navigation and air traffic control systems. However, because they operate by transmitting radio waves from satellites to receivers on the ground, in aircraft, or in other satellites, they are very sensitive to ionospheric disturbances. Significant errors can result when signals are reflected, refracted and slowed by disturbed ionospheric conditions.
Electric power companies are also affected by space weather disturbances because their long power lines are susceptible to electric currents induced by the dramatic changes in high-altitude ionospheric currents occurring during geomagnetic storms. Surges in power lines from ground induced currents (GICs) can cause massive network failures and permanent damage to expensive equipment. It is estimated that the March 1989 Hydro-Quebec power black-out, which was caused by a space weather disturbance, cut electric power to several million people.
With accurate early warning, spacecraft operators can take effective remedial action, such as phased shut downs of components where the most sensitive elements are turned off first and the other components are shut down closer to the predicted onset of the event. Other remedial actions include downloading spacecraft memory to ground-based memory; shutting down all spacecraft systems except those necessary for real-time tracking; increasing real-time monitoring of satellite operations for anomalies; delaying major changes in vehicle potential caused by turning on/off susceptible components; and calculating the best time to adjust a low earth orbit for drag. For military communications, redundant transmissions could be scheduled along with real-time human monitoring as a check of communication integrity. For space stations and shuttles, extra-vehicular activity could be curtailed, launches could be delayed or early landings planned to avoid a disturbance.
Such remedial actions are currently impractical due to the generally short lead time (approximately one hour) and overwhelming inaccuracy (over 80 percent false alarms) of space weather disturbance predictions. If operators were given an accurate warning at least several hours in advance of a space weather event, they would have a great deal more flexibility in developing and implementing strategies for protecting their spacecraft, systems, and/or astronauts. In addition, power companies could, for example, reduce the load on transmission circuits, confidently reset tripped protective relays on power networks, selectively ground capacitor banks to prevent large potential drops and delay power station maintenance and equipment replacement. Telecommunication companies could, for example, look for alternate frequencies for transmissions and effect plans to minimize communications outages.
The space weather forecasts provided by the National Oceanic and Atmospheric Administration""s (NOAA""s) Space Environment Center (SEC), the civilian office responsible for space weather forecasts, demonstrate the need for improvement that this invention addresses. Until several years ago, these forecasts were made entirely xe2x80x9cby eye.xe2x80x9d Operators would examine the raw data (primarily solar magnetic field, x-ray, and optical data) and then, based on intuition and experience, issue forecasts. According to the SEC""s own statistics, only 30% of the storms that they forecast actually occurred. There are also many false negatives (i.e., times when they do not forecast storms that do occur) and the generally brief forecast horizon often does not provide sufficient time for effective remedial action.
Recently, others have attempted to generate more xe2x80x98objectivexe2x80x99 forecasts based at least in part on solar wind and interplanetary magnetic field (IMF) data obtained from the Advance Composition Explorer (ACE) and the WIND spacecraft. Both these spacecraft are very close to the Earth (compared to the distance between the Earth and the sun) and therefore forecasts based on their measurements of solar wind and IMF have a very short lead time. Typically, these systems produce forecasts that have a lead time of one hour or less and often they are ex post facto (i.e. they generate a xe2x80x9cpredictionxe2x80x9d after the event has already begun to disturb the geophysical environment).
Still other forecasting approaches rely upon data from solar event observations, inputting these data into various theoretical models that attempt to predict how the solar events, and their associated shock waves, will propagate through space and effect space weather. The Wang-Sheeley model, the Interplanetary Shock Propagation Model (ISPM) (see Dryer, M. 1998, xe2x80x9cMultidimensional simulation of solar-generated disturbances: Space weather forecasting of geomagnetic storms,xe2x80x9d AIAA Journal, 36, 365-370), and the Shock Time Of Arrival (STOA) model (see Smart, D. F. and Shea, M. A. 1985, xe2x80x9cA simplified model for timing the arrival of solar flare-initiated shocks,xe2x80x9d Journal of Geophysical Research, 90, 183-190) are examples of various theoretical models. These approaches have met with limited success due in part to the difficulty of accurately modeling the propagation of solar events through space and often in part to the lack of complete data on the solar events themselves.
It has been recognized that there is an association between SEP events and subsequent geomagnetic storms. SEPs are created when a large disturbance occurs on the sun and as the disturbance propagates through space. Some of these particles travel towards distant locations (e.g. the Earth, spacecraft, etc.) much more rapidly than the interplanetary shocks that cause many space weather events. They thus may potentially extend the space weather forecast horizon to several hours in advance of a storm and, at times, even a day or more in advance.
Past attempts to use SEPs for space weather prediction have been limited. For example, J. Joselyn described a simplistic technique for forecasting geomagnetic activity. She compared a single measure of SEP activity in only one energy channel to a set threshold. In particular, she looked at SEP events in which a flux of more than 10 protons per centimeter/second of energies exceeding 10 MeV (million electron-volts) occurred for at least 30 minutes; i.e., See Joselyn, J. 1995. Geomagnetic Activity Forecasting: The State of the Art. Reviews of Geophysics, 33, 3. Based on that criterion, she determined that between 1976 and 1989 such events preceded geomagnetic storms (Ap greater than 30, where Ap is the well known global geomagnetic index) within 2-3 days 62% of the time. Joselyn also found that events with peak energetic particle fluxes exceeding 100 flux units preceded geomagnetic storms 75% of the time. Joselyn did not discuss the number or percentage of geomagnetic storms that a forecast based on such events would miss. Joselyn only compared SEP flux to a simple threshold, i.e., a single SEP data value. This simple single point comparison is too simplistic for useful prediction.
More recently, Q. Fan and J. Tian have used measures derived from two SEP values (e.g., the rise rate of SEP flux over time) as inputs to a neural network to attempt to classify the intensity of geomagnetic storms based in part on SEP data. See Fan, Q. and Tian, J. 1998, Prediction of geomagnetic storms following solar proton events (SPEs) with a back-propagation neural network, xe2x80x9cPrediction of Geomagnetic Storms Following Solar Proton Events (SPEs) With a BP Neural Network,xe2x80x9d AI Applications in Solar-Terrestrial Physics. Proceedings of ESA Workshop (WPP-148), edited by I. Sandahl and E. Jonsson, pp. 163-166. Each SEP (proton and electron) flux rise rate was based on only two SEP flux values, the background flux value and the peak flux value. Although Fan and Tian thus begin to recognize the value of time variations in SEP data, they, and Joselyn, failed to capture the potential of solar energetic particles as a space weather prediction tool.
Previous attempts at using SEPs in space weather forecasting have met with only limited success for many reasons. First, the prior work based predictions on only one SEP data point (a threshold or peak value) and/or measures derived from two SEP data points (such as rise rate). They therefore are not capable of identifying complex patterns in SEP data, associated with space weather events, that require consideration of three or more data points. Second, the prior work was based on analysis of only SEP data preceding space weather events, but not of SEP data preceding non-events; any system that does not take into account non-events is susceptible to false alarms and is unable to give all clear signals. Third, the prior work does not recognize the fundamental importance of recent and/or cyclical variations in SEP data (and solar, interplanetary and geophysical activity), such as variations that occur across different phases of the solar cycle. Fourth, the prior work does not provide any indication of a confidence level, such as a numerical confidence index, in a forecast. Fifth, the prior work was unable to provide a forecast while another event was in progress. Sixth, the prior work was unable to meaningfully update forecasts as new data came in.
It is therefore an object of the present invention to provide improved and timely space weather forecasts based on real time SEP data and solar, interplanetary and geophysical data.
It is a further object of the present invention to provide space weather forecasts based on the identification of complex patterns in SEP data requiring consideration of three or more different SEP data values.
It is another object of the present invention to provide a space weather forecasting system that identifies SEP data, and solar, interplanetary and geophysical data, associated with space weather non-events.
It is yet another object of the present invention to provide space weather forecasts that take into consideration recent and/or cyclical variations, such as variations due to the phase of the solar cycle, in SEP data and solar, interplanetary and geophysical data.
It is yet another object of the present invention to provide space weather forecasts that can be meaningfully updated as new data are made available and as the system is dynamically modified (e.g., via data feedback, etc.).
It is yet another object of the present invention to provide a system capable of generating space weather forecasts during periods when another space weather event is already in progress.
It is yet another object of the present invention to provide a space weather forecasting system capable of generating a numerical index representing a confidence level associated with a space weather forecast.
It is yet another object of the present invention to provide a space weather forecasting system capable of working as a xe2x80x9chybrid system,xe2x80x9d whereby different parts of the system are specialized for different types of prediction and thus the overall system accuracy is improved.
It is yet another object of the present invention to provide a space weather forecasting system capable of being modified as a result of current forecasting accuracy/inaccuracy to increase future forecasting accuracy.
The above and other objects are realized by the system and method of the present invention. Briefly, the present invention provides a system and method of forecasting space weather events based on identifying complex patterns defined by three or more SEP data values and associated patterns in solar, interplanetary or geophysical data. The present invention further identifies data patterns associated with space weather non-events, as well as those patterns associated with events. In addition, the patterns identified may change depending on recent or cyclic variations in solar, interplanetary or geophysical activity, such as variations associated with the phase of the solar cycle.
Three embodiments of the present invention are described below: (1) a template-based embodiment, (2) an expert system-based embodiment, and (3) a neural network based embodiment. The template-based embodiment predicts space weather based on a comparison of current SEP data (and other solar, interplanetary or geophysical data of interest) with historically derived xe2x80x9ctemplates,xe2x80x9d each containing three or more SEP data measurements (and other data of interest) associated with the presence or absence of a particular type of space weather event. Separate templates are provided, where appropriate, for different recent and/or cyclic variations in solar, interplanetary, or geophysical activity, such as, but not limited to, variations associated with the phase of the solar cycle. The expert system-based embodiment predicts space weather based on a set of rules that identify patterns in SEP data comprising three or more data points. Such patterns include (i) a peak in SEP data and (ii) a steep rise or peak in x-rays followed by a steep rise or peak in SEPs. Again, separate rules are provided, where appropriate, for recent and/or cyclic variations in solar, interplanetary or geophysical activity. The neural network embodiment predicts space weather based on the input of three or more current SEP data values, possibly together with solar, interplanetary or geophysical activity data values, and, where appropriate, information regarding recent and/or cyclic variations in solar, interplanetary or geophysical activity. It is trained with data from quiet weather states as well as stormy states and, where appropriate, it can be trained with information regarding recent and/or cyclic variations in solar, interplanetary, or geophysical activity. Alternatively, where appropriate, separate neural networks may be used for different phases associated with recent and/or cyclic variations in activity.
More particularly, in the template-based embodiment, the system forecasts space weather events based on comparisons of real-time data with historically derived xe2x80x9ctemplates.xe2x80x9d These templates contain representations of activity associated with a particular type of space weather event or with a non-event. For example, three templates might be used, each representing the SEP activity before storms of different severities. Each of these templates may consist of ten hourly measurements of the number of SEPs having energy  greater than 1 MeV for a particular phase of the solar cycle. This embodiment then finds the best match between the last ten hours of real time SEP data and the set of templates associated with the current phase of the solar cycle. The template that most closely matches the new data is used to determine the current forecast. As more data are obtained (for example, over the next few hours), new comparisons are made and the forecast updated. Additionally, a template may include other data of interest, as identified below, such as x-ray data. The system then finds, in this example, the template that most closely matches the recent SEP and x-ray data.
In the expert system embodiment, space weather forecasts are based on a set of rules that identify patterns in SEP data comprising three or more data points and associated patterns in solar, interplanetary or geophysical data. For example, a rule may involve the detection of a xe2x80x9cpeakxe2x80x9d (i.e. a pattern of xe2x80x9clow-high-lowxe2x80x9d) in SEP data, having particular characteristics based on the current phase of the solar cycle (i.e., the particular characteristics of the peak are derived from earlier data associated with a phase of the solar cycle that is the same as the current phase). Alternatively, a rule may involve a relationship between SEP and other data, such as the detection of a peak in x-ray data followed by a steep rise or peak in SEP data. Still further, a rule may involve modifying SEP and other data, by for example xe2x80x9cblurringxe2x80x9d it, to filter out anomalous or insignificant measurements.
In the neural network embodiment, one or more neural networks are trained with three or more SEP data items associated with space weather events, possibly together with associated solar, interplanetary or geophysical data, and, where appropriate, solar cycle phase data, or other data identifying a phase of a recent or cyclic variation in data. The neural networks are also trained with non-event data for more accurately predicting times when no space weather event will occur and for signaling all clear. The neural networks are then used to generate forecasts based on recent SEP data and, where appropriate, solar cycle phase. Alternatively, the neural network may additionally be trained with other solar, interplanetary and geophysical data of interest, such as, but not limited to, x-ray data.
In general, these embodiments of the invention are not limited to space weather forecasts based upon SEPs or solar data in a particular energy band or having a particular flux level. Nor are they limited to a particular species of particle (protons, electrons, alphas, oxygen ions, iron ions) or waves (x-ray, radio waves, microwaves, etc). Nor are they limited to the use of actual SEP or solar or interplanetary or geophysical data -modeled or simulated data can be used, or in many instances, precursory signals such as microwave bursts, disappearing filaments, sunspot activity, helioseismology parameters, etc. can be used as proxies for actual data.
Furthermore, the template embodiment is not limited to a specific technique for creating the templates, nor to a particular number of templates, nor to the use of a particular technique for comparing new data to the templates. Also, the expert system is not limited to any particular static (or even dynamic) set of rules that determines how the identification and/or classification of data satisfying a particular set of criteria is accomplished. Finally, the neural network embodiment is not limited to a specific technique for training the neural networks nor to a particular neural network architecture.
For each of the embodiments, one or more inputs could be from the results of another template system, expert system or neural network, or from a hybrid combination of these.